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| | *[[How to import the sparse data of vsm to weka]] | | *[[How to import the sparse data of vsm to weka]] |
| | | | |
| − | ==Document classification of Sougou data == | + | ==Test== |
| − | * DATA
| + | [[Sougou data]] |
| − | :* Data from SougouLab [http://www.sogou.com/labs/dl/c.html],using SogouC.reduced(30M)
| + | |
| − | :* 9-Classes:财经,IT,健康,体育,旅游,教育,招聘,文化,军事
| + | |
| − | :* train and test: train(),test(),dev()
| + | |
| − | *Text preprocessing
| + | |
| − | :* Segment word using wordlist of 9W.(tencent)
| + | |
| − | :* Remove stop word.stop_wordlist is
| + | |
| − | :*
| + | |
| − | *Some Tools
| + | |
| − | :* weka
| + | |
| − | :* scw
| + | |
| − | :* google word2ve
| + | |
| − | :* LDA
| + | |
| − | ===VSM Test===
| + | |
| − | *Data
| + | |
| − | :* dimension:9402
| + | |
| − | *Method
| + | |
| − | :* document reprenstion: use the tf-idf weight for word weight
| + | |
| − | :* classifier: Native Bayes
| + | |
| − | *Result
| + | |
| − | | + | |
| − | {| border="2px"
| + | |
| − | |+ classification result
| + | |
| − | |-
| + | |
| − | ! !! 财经!! IT!! 健康!! 体育!! 旅游 !!教育 !! 招聘!! 文化!!军事!!sum
| + | |
| − | |-
| + | |
| − | ! ACC-test
| + | |
| − | | 0.72139 || 0.72139 || 0.75124 || 0.82089 || 0.79602 || 0.61194 || 0.70647 || 0.79104 || 0.72913
| + | |
| − | |-
| + | |
| − | ! ACC-train
| + | |
| − | | 0.678 || 0.718 || 0.708 || 0.708 || 0.73
| + | |
| − | |-
| + | |
| − | |}
| + | |
| − | | + | |
| − | ===LDA Test===
| + | |
| − | ===Word2vec Test===
| + | |
